Transfer Learning Method Towards Lung and Colon Cancers Automated Analysis in Histopathological Images

Lung and colon cancers are among the top causes of death and morbidity in humans. Their simultaneous growth within organs can have a detrimental impact on human life. If cancer is not detected early, it is likely to spread to both of those organs. In this research, transfer learning method based on a numerous model is presented and compared to automate lung and colon cancers diagnosis in histopathological data. Many studies in this field employ the LC25000 dataset. In fact, it includes 25000 histopathological images belonging to 5 distinct classes which are benign colonic tissue, colon adenocarcinoma, lung squamous cell carcinoma, lung adenocarcinoma, and benign lung tissue. For the transfer Learning method, the used models which were compared are EfficientNetsV2 from B0 to B3, and Small, MobileNetV2, ResNetV2, and Xception. The best results were reached when Employing EfficientNetV2B1. The scored accuracy, F1-score, AUC are equal to 1. Moreover, the loss is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$6.06. 10^{-5}$</tex>, respectively. Furthermore, the accuracy, F1score, AUC, Sensitivity, Specificity, attained 1 for the totality of the 5 classes. The proposed method with a transfer learning strategy is extremely efficient for lung and colon cancer automated diagnosis, outperforming several current methods. This method might help doctors provide more accurate diagnoses and improve patient outcomes.